



import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


_snake_case						=    16
_snake_case						=    32






def 							A							(   _lowerCamelCase							,					_lowerCamelCase = 16    ):





							'''simple docstring'''




							_lowerCAmelCase	:				Any					  =       AutoTokenizer.from_pretrained("bert-base-cased"    )
							_lowerCAmelCase	:				Optional[int]					  =       load_dataset("glue"							,					"mrpc"    )

							def tokenize_function(_lowerCamelCase    ):
														# max_length=None => use the model max length (it's actually the default)
														_lowerCAmelCase	:				Any					  =       tokenizer(examples["sentence1"]							,					examples["sentence2"]							,					truncation=a_							,					max_length=a_    )
														return outputs

							# Apply the method we just defined to all the examples in all the splits of the dataset
							# starting with the main process first:
							with accelerator.main_process_first():
														_lowerCAmelCase	:				List[str]					  =       datasets.map(
														    a_							,					batched=a_							,					remove_columns=["idx", "sentence1", "sentence2"]							,					)

							# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
							# transformers library
							_lowerCAmelCase	:				int					  =       tokenized_datasets.rename_column("label"							,					"labels"    )

							def collate_fn(_lowerCamelCase    ):
														# On TPU it's best to pad everything to the same length or training will be very slow.
														_lowerCAmelCase	:				Optional[Any]					  =       128 if accelerator.distributed_type == DistributedType.TPU else None
														# When using mixed precision we want round multiples of 8/16
														if accelerator.mixed_precision == "fp8":
																					_lowerCAmelCase	:				str					  =       16
														elif accelerator.mixed_precision != "no":
																					_lowerCAmelCase	:				Any					  =       8
														else:
																					_lowerCAmelCase	:				List[str]					  =       None

														return tokenizer.pad(
														    a_							,					padding="longest"							,					max_length=a_							,					pad_to_multiple_of=a_							,					return_tensors="pt"							,					)

							# Instantiate dataloaders.
							_lowerCAmelCase	:				int					  =       DataLoader(
							    tokenized_datasets["train"]							,					shuffle=a_							,					collate_fn=a_							,					batch_size=a_							,					drop_last=a_    )
							_lowerCAmelCase	:				List[Any]					  =       DataLoader(
							    tokenized_datasets["validation"]							,					shuffle=a_							,					collate_fn=a_							,					batch_size=a_							,					drop_last=(accelerator.mixed_precision == "fp8")							,					)

							return train_dataloader, eval_dataloader






def 							A							(   _lowerCamelCase							,					_lowerCamelCase    ):





							'''simple docstring'''




							_lowerCAmelCase	:				Optional[Any]					  =       Accelerator(cpu=args.cpu							,					mixed_precision=args.mixed_precision    )
							# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
							_lowerCAmelCase	:				Optional[int]					  =       config["lr"]
							_lowerCAmelCase	:				Optional[Any]					  =       int(config["num_epochs"]    )
							_lowerCAmelCase	:				List[str]					  =       int(config["seed"]    )
							_lowerCAmelCase	:				Union[str, Any]					  =       int(config["batch_size"]    )

							_lowerCAmelCase	:				List[str]					  =       evaluate.load("glue"							,					"mrpc"    )

							# If the batch size is too big we use gradient accumulation
							_lowerCAmelCase	:				str					  =       1
							if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
														_lowerCAmelCase	:				Dict					  =       batch_size // MAX_GPU_BATCH_SIZE
														_lowerCAmelCase	:				Optional[int]					  =       MAX_GPU_BATCH_SIZE

							set_seed(a_    )
							_lowerCAmelCase	:				Tuple					  =       get_dataloaders(a_							,					a_    )
							# Instantiate the model (we build the model here so that the seed also control new weights initialization)
							_lowerCAmelCase	:				Any					  =       AutoModelForSequenceClassification.from_pretrained("bert-base-cased"							,					return_dict=a_    )

							# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
							# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
							# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
							_lowerCAmelCase	:				List[Any]					  =       model.to(accelerator.device    )
							# Instantiate optimizer
							_lowerCAmelCase	:				Dict					  =       AdamW(params=model.parameters()							,					lr=a_    )

							# Instantiate scheduler
							_lowerCAmelCase	:				int					  =       get_linear_schedule_with_warmup(
							    optimizer=a_							,					num_warmup_steps=100							,					num_training_steps=(len(a_    ) * num_epochs) // gradient_accumulation_steps							,					)

							# Prepare everything
							# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
							# prepare method.

							_lowerCAmelCase	:				Dict					  =       accelerator.prepare(
							    a_							,					a_							,					a_							,					a_							,					a_    )

							# Now we train the model
							for epoch in range(a_    ):
														model.train()
														for step, batch in enumerate(a_    ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device    )
																					_lowerCAmelCase	:				Union[str, Any]					  =       model(**a_    )
																					_lowerCAmelCase	:				List[str]					  =       outputs.loss
																					_lowerCAmelCase	:				int					  =       loss / gradient_accumulation_steps
																					accelerator.backward(a_    )
																					if step % gradient_accumulation_steps == 0:
																												optimizer.step()
																												lr_scheduler.step()
																												optimizer.zero_grad()

														model.eval()
														for step, batch in enumerate(a_    ):
																					# We could avoid this line since we set the accelerator with `device_placement=True`.
																					batch.to(accelerator.device    )
																					with torch.no_grad():
																												_lowerCAmelCase	:				List[str]					  =       model(**a_    )
																					_lowerCAmelCase	:				List[Any]					  =       outputs.logits.argmax(dim=-1    )
																					_lowerCAmelCase	:				Optional[int]					  =       accelerator.gather_for_metrics((predictions, batch["labels"])    )
																					metric.add_batch(
																					    predictions=a_							,					references=a_							,					)

														_lowerCAmelCase	:				int					  =       metric.compute()
														# Use accelerator.print to print only on the main process.
														accelerator.print(F"epoch {epoch}:"							,					a_    )






def 							A							(   ):





							'''simple docstring'''




							_lowerCAmelCase	:				Optional[Any]					  =       argparse.ArgumentParser(description="Simple example of training script."    )
							parser.add_argument(
							    "--mixed_precision"							,					type=a_							,					default=a_							,					choices=["no", "fp16", "bf16", "fp8"]							,					help="Whether to use mixed precision. Choose"
							    "between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
							    "and an Nvidia Ampere GPU."							,					)
							parser.add_argument("--cpu"							,					action="store_true"							,					help="If passed, will train on the CPU."    )
							_lowerCAmelCase	:				Optional[Any]					  =       parser.parse_args()
							_lowerCAmelCase	:				Dict					  =       {"lr": 2e-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
							training_function(a_							,					a_    )


if __name__ == "__main__":
						main()







'''simple docstring'''






import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowerCAmelCase									=   random.Random()






def   __A    (				a_    :	Union[str, Any]   ,a_    :	Tuple=1.0   ,a_    :	Optional[int]=None   ,a_    :	Union[str, Any]=None						):
		if rng is None:
				lowerCAmelCase  :     str       						= global_rng

		lowerCAmelCase  :     Any       						= []
		for batch_idx in range(shape[0]						):
				values.append([]						)
				for _ in range(shape[1]						):
						values[-1].append(rng.random() * scale						)

		return values
class 		lowerCamelCase	(   unittest.TestCase							):



	def __init__( self		,   a_		,   a_=7		,   a_=400		,   a_=2_000		,   a_=1		,   a_=0.0		,   a_=16_000		,   a_=True		,   a_=True		,   ):
			lowerCAmelCase  :     Dict       						= parent
			lowerCAmelCase  :     Optional[int]       						= batch_size
			lowerCAmelCase  :     Dict       						= min_seq_length
			lowerCAmelCase  :     Dict       						= max_seq_length
			lowerCAmelCase  :     Optional[Any]       						= (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
			lowerCAmelCase  :     Union[str, Any]       						= feature_size
			lowerCAmelCase  :     Tuple       						= padding_value
			lowerCAmelCase  :     Dict       						= sampling_rate
			lowerCAmelCase  :     int       						= return_attention_mask
			lowerCAmelCase  :     Optional[Any]       						= do_normalize



	def 	_lowerCamelCase     ( self						):
			return {
			    "feature_size": self.feature_size,
			    "padding_value": self.padding_value,
			    "sampling_rate": self.sampling_rate,
			    "return_attention_mask": self.return_attention_mask,
			    "do_normalize": self.do_normalize,
			}





	def 	_lowerCamelCase     ( self		,   a_=False		,   a_=False						):
			def _flatten(a_						):
					return list(itertools.chain(*a_						)						)

			if equal_length:
					lowerCAmelCase  :     List[Any]       						= floats_list((self.batch_size, self.max_seq_length)						)
			else:
					# make sure that inputs increase in size
					lowerCAmelCase  :     Any       						= [
					    _flatten(floats_list((x, self.feature_size)						)						)
					    for x in range(self.min_seq_length		,   self.max_seq_length		,   self.seq_length_diff						)
					]

			if numpify:
					lowerCAmelCase  :     List[str]       						= [np.asarray(a_						) for x in speech_inputs]

			return speech_inputs






class 		lowerCamelCase	(   _A   , unittest.TestCase							):
	snake_case_   = WavaVecaFeatureExtractor



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Tuple       						= WavaVecaFeatureExtractionTester(self						)



	def 	_lowerCamelCase     ( self		,   a_						):
			self.assertTrue(np.all(np.mean(a_		,   axis=0						) < 1e-3						)						)
			self.assertTrue(np.all(np.abs(np.var(a_		,   axis=0						) - 1						) < 1e-3						)						)



	def 	_lowerCamelCase     ( self						):
			# Tests that all call wrap to encode_plus and batch_encode_plus
			lowerCAmelCase  :     int       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			# create three inputs of length 800, 1000, and 1200
			lowerCAmelCase  :     str       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]
			lowerCAmelCase  :     Dict       						= [np.asarray(a_						) for speech_input in speech_inputs]

			# Test not batched input
			lowerCAmelCase  :     Optional[int]       						= feat_extract(speech_inputs[0]		,   return_tensors="np"						).input_values
			lowerCAmelCase  :     Any       						= feat_extract(np_speech_inputs[0]		,   return_tensors="np"						).input_values
			self.assertTrue(np.allclose(a_		,   a_		,   atol=1e-3						)						)

			# Test batched
			lowerCAmelCase  :     Any       						= feat_extract(a_		,   return_tensors="np"						).input_values
			lowerCAmelCase  :     int       						= feat_extract(a_		,   return_tensors="np"						).input_values
			for enc_seq_a, enc_seq_a in zip(a_		,   a_						):
					self.assertTrue(np.allclose(a_		,   a_		,   atol=1e-3						)						)

			# Test 2-D numpy arrays are batched.
			lowerCAmelCase  :     Dict       						= [floats_list((1, x)						)[0] for x in (800, 800, 800)]
			lowerCAmelCase  :     Tuple       						= np.asarray(a_						)
			lowerCAmelCase  :     int       						= feat_extract(a_		,   return_tensors="np"						).input_values
			lowerCAmelCase  :     Union[str, Any]       						= feat_extract(a_		,   return_tensors="np"						).input_values
			for enc_seq_a, enc_seq_a in zip(a_		,   a_						):
					self.assertTrue(np.allclose(a_		,   a_		,   atol=1e-3						)						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     int       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     Any       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]

			lowerCAmelCase  :     Union[str, Any]       						= ["longest", "max_length", "do_not_pad"]
			lowerCAmelCase  :     Optional[int]       						= [None, 1_600, None]
			for max_length, padding in zip(a_		,   a_						):
					lowerCAmelCase  :     List[Any]       						= feat_extract(a_		,   padding=a_		,   max_length=a_		,   return_tensors="np"						)
					lowerCAmelCase  :     Any       						= processed.input_values

					self._check_zero_mean_unit_variance(input_values[0][:800]						)
					self.assertTrue(input_values[0][800:].sum() < 1e-6						)
					self._check_zero_mean_unit_variance(input_values[1][:1_000]						)
					self.assertTrue(input_values[0][1_000:].sum() < 1e-6						)
					self._check_zero_mean_unit_variance(input_values[2][:1_200]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     Optional[int]       						= range(800		,   1_400		,   200						)
			lowerCAmelCase  :     Tuple       						= [floats_list((1, x)						)[0] for x in lengths]

			lowerCAmelCase  :     str       						= ["longest", "max_length", "do_not_pad"]
			lowerCAmelCase  :     Any       						= [None, 1_600, None]

			for max_length, padding in zip(a_		,   a_						):
					lowerCAmelCase  :     Dict       						= feat_extract(a_		,   max_length=a_		,   padding=a_						)
					lowerCAmelCase  :     int       						= processed.input_values

					self._check_zero_mean_unit_variance(input_values[0][:800]						)
					self._check_zero_mean_unit_variance(input_values[1][:1_000]						)
					self._check_zero_mean_unit_variance(input_values[2][:1_200]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     List[Any]       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     Tuple       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]
			lowerCAmelCase  :     Dict       						= feat_extract(
			    a_		,   truncation=a_		,   max_length=1_000		,   padding="max_length"		,   return_tensors="np"						)
			lowerCAmelCase  :     Union[str, Any]       						= processed.input_values

			self._check_zero_mean_unit_variance(input_values[0, :800]						)
			self._check_zero_mean_unit_variance(input_values[1]						)
			self._check_zero_mean_unit_variance(input_values[2]						)



	def 	_lowerCamelCase     ( self						):
			lowerCAmelCase  :     Optional[int]       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     Tuple       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]
			lowerCAmelCase  :     Union[str, Any]       						= feat_extract(
			    a_		,   truncation=a_		,   max_length=1_000		,   padding="longest"		,   return_tensors="np"						)
			lowerCAmelCase  :     Dict       						= processed.input_values

			self._check_zero_mean_unit_variance(input_values[0, :800]						)
			self._check_zero_mean_unit_variance(input_values[1, :1_000]						)
			self._check_zero_mean_unit_variance(input_values[2]						)

			# make sure that if max_length < longest -> then pad to max_length
			self.assertTrue(input_values.shape == (3, 1_000)						)

			lowerCAmelCase  :     List[str]       						= [floats_list((1, x)						)[0] for x in range(800		,   1_400		,   200						)]
			lowerCAmelCase  :     Union[str, Any]       						= feat_extract(
			    a_		,   truncation=a_		,   max_length=2_000		,   padding="longest"		,   return_tensors="np"						)
			lowerCAmelCase  :     Optional[Any]       						= processed.input_values

			self._check_zero_mean_unit_variance(input_values[0, :800]						)
			self._check_zero_mean_unit_variance(input_values[1, :1_000]						)
			self._check_zero_mean_unit_variance(input_values[2]						)

			# make sure that if max_length > longest -> then pad to longest
			self.assertTrue(input_values.shape == (3, 1_200)						)



	@require_torch
	def 	_lowerCamelCase     ( self						):
			import torch

			lowerCAmelCase  :     Optional[int]       						= self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()						)
			lowerCAmelCase  :     List[Any]       						= np.random.rand(100						).astype(np.floataa						)
			lowerCAmelCase  :     List[Any]       						= np_speech_inputs.tolist()

			for inputs in [py_speech_inputs, np_speech_inputs]:
					lowerCAmelCase  :     Union[str, Any]       						= feature_extractor.pad([{"input_values": inputs}]		,   return_tensors="np"						)
					self.assertTrue(np_processed.input_values.dtype == np.floataa						)
					lowerCAmelCase  :     Tuple       						= feature_extractor.pad([{"input_values": inputs}]		,   return_tensors="pt"						)
					self.assertTrue(pt_processed.input_values.dtype == torch.floataa						)





	@slow
	@require_torch
	def 	_lowerCamelCase     ( self						):
			# this test makes sure that models that are using
			# group norm don't have their feature extractor return the
			# attention_mask
			for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
					lowerCAmelCase  :     Any       						= WavaVecaConfig.from_pretrained(a_						)
					lowerCAmelCase  :     List[str]       						= WavaVecaFeatureExtractor.from_pretrained(a_						)

					# only "layer" feature extraction norm should make use of
					# attention_mask
					self.assertEqual(feat_extract.return_attention_mask		,   config.feat_extract_norm == "layer"						)



